Yao Chen’s scientific contributions

What is this page?

This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (1)

Fig. 1: Specific implementation of the multi-space self-attention mechanism
Fig. 2: Frame structure of the joint attention mechanism based on EEG and eye movement Joint attention mechanism first through linear changes from the attention layer with attention and the EEG features map to the same dimension space joint feature tensor, from the joint features the joint attention vector, the joint attention vector can highlight the highly related to the emotions and can better fusion with EEG advanced features of the important features. Allocation of joint attention vectors to eye movement features enables further screening of eye movement features. Flowchart of the joint attention mechanism based on EEG and eye movement:
Application of artificial intelligence in the field of psychological evaluation and intervention of college students
  • Article
  • Full-text available

April 2025

·

15 Reads

International Scientific Technical and Economic Research

Xurui Liao

·

Yao Chen

·

Heng Wang

·

[...]

·

Junchen Li

With the rapid development of artificial intelligence (AI) technology, its application in the field of mental health is increasingly extensive. As an important group in society, college students &039; mental health problems have attracted much attention. This paper discusses the present situation of artificial intelligence intervention in college students &039; psychological evaluation, and analyzes the application limitations. Aiming at the speed and accuracy of evaluation, a multi-modal model based on feature fusion and decision fusion is proposed to integrate multi-dimensional data to improve the efficiency and accuracy of evaluation. The results show that the performance of the model is better than that of the traditional method, which pro vides support for the intelligentization of college students &039; mental health services. The conclusion is that the model provides an effective way to solve the limitation of evaluation. **************** ACKNOWLEDGEMENTS**************** This work was supported by the Innovation Training Project of Guangdong Ocean University (Project No. CXXL2024113).

Download